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"Part
of being data driven, particularly when you are building a product, is stepping
back and saying data doesn't always have all the answer," Cathy Tanimura,
senior director of analytics and data science at Strava, remarked as she began
her presentation at DATAx San Francisco. "Instead it is about really
thinking about what the data can do to help us understand the opportunity."

Tanimura
urged companies to instead use data to help them "find a beacon through
the sea" and uncover good ideas.

"The
reality is when you go through a journey, you're going to have some dead ends
and challenges along the way," she observed.

However, on
the point of data application she noted that, when putting an idea forward,
executives must always think about what data they have to guide them whether to
go forward or not.

Tanimura
outlined three lessons she had learned when it comes to using data to enhance
the customer experience.

Find the insights that matter

Understanding
customers is key to creating loyal users of your product, Tanimura explained, and
this means cutting through the noise and uncovering the insights which will be
the most helpful. She noted that for Strava this was discovering the social
aspect to its platform and using this to understand users.

For
example, the firm's data uncovered that people who exercised with others reported
more data on the app, with those in clubs being three times as likely to upload
data onto the app.

Gather data from many places

Behavioral
signals are one of the ways that Strava gathers its data.

"We
can see people following each other, we can see people viewing each other's
activities and commenting, so we know they are being social, but this does not
always help us with products," Tanimura said, echoing her earlier
sentiment that it is key to know when to step back.

She
explained how her team had also looked at their own experiences for insight, but
quickly added with this approach companies have to be particularly careful, as
their team will always only represent certain subsection of society. Community
input was the area Strava had found the most useful.

"We
have community forums, we have people telling us what they don't like which is
helpful," she said. "We also get requests from what people would like
us to add to the platform."

Layering ML/AI on top of your strengths

When it
comes to applying AI and machine learning in business, Tanimura reiterates the
importance of taking a moment to consider its benefit from a distance.

"The
important thing is to bring it back to: Where is this business value? What does
this add?"

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